Accuracy is taken to be the highest reported in [1]}, the highest reported in the paper, or trained from scratch using the EfficientNet-B0 hyperparameters used in the [1]} repository, see Table REF . This is the case for EfficientNet-B0 (our training), MobileNetV2, MnasNet, SPNASNet and FBNet. OFA/Scratch is the
“flops@389M_top1@79.1_finetune@75” model from  [3]} trained from scratch using the hyperparameters used for EfficientNet-B0 in [1]}. Note that these baselines are competitive. MobileNetV2 for example, typically has an accuracy of around 72\(\%\) , while the training in [1]} pushes that to 73\(\%\) . ResNet50 is typically at 76\(\%\) , but reaches 79\(\%\)  using the training proposed in [1]}. ProxylessNas [7]} and DNA's [8]} accuracy is taken from their respective papers.
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